mlsanityData to 14 Oct 2026

Visual Wake Words Dataset

arXiv 1906.05721 ↗First posted 12 Jun 2019cs.CV eess.IVcited by 46

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Abstract (arXiv)

The emergence of Internet of Things (IoT) applications requires intelligence on the edge. Microcontrollers provide a low-cost compute platform to deploy intelligent IoT applications using machine learning at scale, but have extremely limited on-chip memory and compute capability. To deploy computer vision on such devices, we need tiny vision models that fit within a few hundred kilobytes of memory footprint in terms of peak usage and model size on device storage. To facilitate the development of microcontroller friendly models, we present a new dataset, Visual Wake Words, that represents a common microcontroller vision use-case of identifying whether a person is present in the image or not, and provides a realistic benchmark for tiny vision models. Within a limited memory footprint of 250 KB, several state-of-the-art mobile models achieve accuracy of 85-90% on the Visual Wake Words dataset. We anticipate the proposed dataset will advance the research on tiny vision models that can push the pareto-optimal boundary in terms of accuracy versus memory usage for microcontroller applications.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jun 2019.

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Citing papers per year: 2021: 9; 2022: 14; 2023: 4; 2024: 8; 2025: 5; 2026: 6.

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  1. 2022On-Device Training Under 256KB Memorycited by 160
  2. 2021MLPerf Tiny Benchmarkcited by 124
  3. 2021MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learningcited by 89
  4. 2022Machine Learning for Microcontroller-Class Hardware: A Reviewcited by 28
  5. 2022Edge Impulse: An MLOps Platform for Tiny Machine Learningcited by 24
  6. 2021Widening Access to Applied Machine Learning with TinyMLcited by 17
  7. 2021Differentiable Network Pruning for Microcontrollerscited by 16
  8. 2022P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applicationscited by 16
  9. 2021On-device Federated Learning with Flowercited by 13
  10. 2022Intelligence at the Extreme Edge: A Survey on Reformable TinyMLcited by 13
  11. 2021Quantization-Guided Training for Compact TinyML Modelscited by 10
  12. 2023AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computingcited by 10
34 more
  1. 2024On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Casecited by 9
  2. 2021AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Acceleratorcited by 7
  3. 2023Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devicescited by 7
  4. 2021TinyFedTL: Federated Transfer Learning on Tiny Devicescited by 6
  5. 2022Accelerating Deep Learning Model Inference on Arm CPUs with Ultra-Low Bit Quantization and Runtimecited by 6
  6. 2023Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraintcited by 6
  7. 2022Machine Learning Sensorscited by 5
  8. 2022T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXitcited by 5
  9. 2024LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronizationcited by 5
  10. 2024TinySV: Speaker Verification in TinyML with On-device Learningcited by 5
  11. 2024On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollerscited by 5
  12. 2024Mixed-precision Neural Networks on RISC-V Cores: ISA extensions for Multi-Pumped Soft SIMD Operationscited by 5
  13. 2021TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNTcited by 4
  14. 2022Toward Efficient Hyperspectral Image Processing inside Camera Pixelscited by 4
  15. 2022Depth Pruning with Auxiliary Networks for TinyMLcited by 4
  16. 2024DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruningcited by 4
  17. 2022Pex: Memory-efficient Microcontroller Deep Learning through Partial Executioncited by 3
  18. 2024MicroFlow: An Efficient Rust-Based Inference Engine for TinyMLcited by 3
  19. 2025Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAscited by 3
  20. 2022Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Imagescited by 2
  21. 2025Searching Neural Architectures for Sensor Nodes on IoT Gatewayscited by 2
  22. 2026An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platformscited by 2
  23. 2022Hardware-aware mobile building block evaluation for computer visioncited by 1
  24. 2022Enabling ISP-less Low-Power Computer Visioncited by 1
  25. 2023Datasheets for Machine Learning Sensorscited by 1
  26. 2024Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applicationscited by 1
  27. 2026BitFair: A 12-nm Bit-Serial CNN Accelerator with Learnable Early Termination and Adaptive Bit Ordering for Ultra-Low-Power XR Visioncited by 1
  28. 2025End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruningcited by 0
  29. 2025Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNLcited by 0
  30. 2025MaRVIn: A Cross-Layer Mixed-Precision RISC-V Framework for DNN Inference, from ISA Extension to Hardware Accelerationcited by 0
  31. 2026LUT-KAN: Segment-wise LUT Quantization for Fast KAN Inferencecited by 0
  32. 2026What changes after deployment? A survey on On-device Learning in TinyMLcited by 0
  33. 2026Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision Systemcited by 0
  34. 2026Hardware-Aware Learned Representation Compression for Distributed In-Sensor Visioncited by 0